Classification of birds and drones by exploiting periodical motions in Doppler spectrum series

With the rapidly growing abuse of drones, monitoring and classification of birds and drones have become a crucial safety issue. With similar low radar cross sections(RCSs), velocities, and heights, drones are usually difficult to be distinguished from birds in radar measurements. In this paper, we propose to exploit different periodical motions of birds and drones from highresolution Doppler spectrum sequences(DSSs) for classification.This paper presents an elaborate feature vector representing the periodic fluctuations of RCS and micro kinematics. Fed by the Doppler spectrum and feature sequence, the long to short-time memory(LSTM) is used to solve the time series classification.Different classification schemes to exploit the Doppler spectrum series are validated and compared by extensive real-data experiments, which confirms the effectiveness and superiorities of the proposed algorithm.

Deep learning framework for time series classification based on multiple imaging and hybrid quantum neural networks

Time series classification(TSC)has attracted a lot of attention for time series data mining tasks and has been applied in various fields.With the success of deep learning(DL)in computer vision recognition,people are starting to use deep learning to tackle TSC tasks.Quantum neural networks(QNN)have recently demonstrated their superiority over traditional machine learning in methods such as image processing and natural language processing,but research using quantum neural networks to handle TSC tasks has not received enough attention.Therefore,we proposed a learning framework based on multiple imaging and hybrid QNN(MIHQNN)for TSC tasks.We investigate the possibility of converting 1D time series to 2D images and classifying the converted images using hybrid QNN.We explored the differences between MIHQNN based on single time series imaging and MIHQNN based on the fusion of multiple time series imaging.Four quantum circuits were also selected and designed to study the impact of quantum circuits on TSC tasks.We tested our method on several standard datasets and achieved significant results compared to several current TSC methods,demonstrating the effectiveness of MIHQNN.This research highlights the potential of applying quantum computing to TSC and provides the theoretical and experimental background for future research.

Accurate Multi-Scale Feature Fusion CNN for Time Series Classification in Smart Factory

Time series classification(TSC)has attracted various attention in the community of machine learning and data mining and has many successful applications such as fault detection and product identification in the process of building a smart factory.However,it is still challenging for the efficiency and accuracy of classification due to complexity,multi-dimension of time series.This paper presents a new approach for time series classification based on convolutional neural networks(CNN).The proposed method contains three parts:short-time gap feature extraction,multi-scale local feature learning,and global feature learning.In the process of short-time gap feature extraction,large kernel filters are employed to extract the features within the short-time gap from the raw time series.Then,a multi-scale feature extraction technique is applied in the process of multi-scale local feature learning to obtain detailed representations.The global convolution operation with giant stride is to obtain a robust and global feature representation.The comprehension features used for classifying are a fusion of short time gap feature representations,local multi-scale feature representations,and global feature representations.To test the efficiency of the proposed method named multi-scale feature fusion convolutional neural networks(MSFFCNN),we designed,trained MSFFCNN on some public sensors,device,and simulated control time series data sets.The comparative studies indicate our proposed MSFFCNN outperforms other alternatives,and we also provided a detailed analysis of the proposed MSFFCNN.

Empirical Study of Classification Process for Two-stage Turbo Air Classifier in Series

The suitable process parameters for a two-stage turbo air classifier are important for obtaining the ultrafine powder that has a narrow particle-size distribution, however little has been published internationally on the classification process for the two-stage turbo air classifier in series. The influence of the process parameters of a two-stage turbo air classifier in series on classification performance is empirically studied by using aluminum oxide powders as the experimental material. The experimental results show the following: 1) When the rotor cage rotary speed of the first-stage classifier is increased from 2 300 r/min to 2 500 r/min with a constant rotor cage rotary speed of the second-stage classifier, classification precision is increased from 0.64 to 0.67. However, in this case, the final ultrafine powder yield is decreased from 79% to 74%, which means the classification precision and the final ultrafine powder yield can be regulated through adjusting the rotor cage rotary speed of the first-stage classifier. 2) When the rotor cage rotary speed of the second-stage classifier is increased from 2 500 r/min to 3 100 r/min with a constant rotor cage rotary speed of the first-stage classifier, the cut size is decreased from 13.16 μm to 8.76 μm, which means the cut size of the ultrafine powder can be regulated through adjusting the rotor cage rotary speed of the second-stage classifier. 3) When the feeding speed is increased from 35 kg/h to 50 kg/h, the 'fish-hook' effect is strengthened, which makes the ultrafine powder yield decrease. 4) To weaken the 'fish-hook' effect, the equalization of the two-stage wind speeds or the combination of a high first-stage wind speed with a low second-stage wind speed should be selected. This empirical study provides a criterion of process parameter configurations for a two-stage or multi-stage classifier in series, which offers a theoretical basis for practical production.

Classification of Vegetation in North Tibet Plateau Based on MODIS Time-Series Data

Based on the 16d-composite MODIS （moderate resolution imaging spectroradiometer）-NDVI（normalized difference vegetation index） time-series data in 2004, vegetation in North Tibet Plateau was classified and seasonal variations on the pixels selected from different vegetation type were analyzed. The Savitzky-Golay filtering algorithm was applied to perform a filtration processing for MODIS-NDVI time-series data. The processed time-series curves can reflect a real variation trend of vegetation growth. The NDVI time-series curves of coniferous forest, high-cold meadow, high-cold meadow steppe and high-cold steppe all appear a mono-peak model during vegetation growth with the maximum peak occurring in August. A decision-tree classification model was established according to either NDVI time-series data or land surface temperature data. And then, both classifying and processing for vegetations were carried out through the model based on NDVI time-series curves. An accuracy test illustrates that classification results are of high accuracy and credibility and the model is conducive for studying a climate variation and estimating a vegetation production at regional even global scale.

Interpretation and Classification of P-Series Recommendations in ITU-R

As ITU-R Recommendations is widely implemented for countries all over the world, the role and status of ITU-R Recommendations are increasingly prominent in the field of radio engineering. ITU and ITU-R Study Groups are summarized. Furthermore, the operating mode of the third study group, and the input documents are interpreted in detail. Lastly, from both wireless system design and electromagnetic compatibility analysis perspective, all of 79 P-series Recommendations are analyzed and classified, and the main contents of each Recommendation are summarized. The above research promote P-series Recommendations are widely used in China.

Classification and Source Materials of Continental Crust Transformation Series Granitoids in South China

The granitoids of the continental crust transformation series in South China may be divided into threetypes: (1) synorogenic migmatic and magmatic type. (2) anorogenic continental crust anatexis type, and (3)syncollision type. Based on the results of Sr and Nd isotopic determinations, the source material compositionof the three types of granitoids is calculated with crust-mantle binary mixing simulation. The calculations indi-cate that the granitoids of the first type consist of 78.6-89.7% upper crust endmember materials and15.0-10.3% depleted mantle endmember materials, the granitoids of the second type are composed of 63.7%upper crust endmember materials and 36.3% depleted mantle endmember materials, and those of the third type100% upper crust endmember materials. Hence. the source material composition of the granitoids of all thethree types is dominated by upper crust endmembers.

Classification of Short Time Series in Early Parkinson’s Disease With Deep Learning of Fuzzy Recurrence Plots

There are many techniques using sensors and wearable devices for detecting and monitoring patients with Parkinson’s disease(PD).A recent development is the utilization of human interaction with computer keyboards for analyzing and identifying motor signs in the early stages of the disease.Current designs for classification of time series of computer-key hold durations recorded from healthy control and PD subjects require the time series of length to be considerably long.With an attempt to avoid discomfort to participants in performing long physical tasks for data recording,this paper introduces the use of fuzzy recurrence plots of very short time series as input data for the machine training and classification with long short-term memory(LSTM)neural networks.Being an original approach that is able to both significantly increase the feature dimensions and provides the property of deterministic dynamical systems of very short time series for information processing carried out by an LSTM layer architecture,fuzzy recurrence plots provide promising results and outperform the direct input of the time series for the classification of healthy control and early PD subjects.

基于ResCNN-BiGRU的四川方言语音识别

由于基于深度卷积神经网络的语音识别模型中缺乏对特定方言音素特征的提取能力,造成方言发音底层特征部分信息丢失,进而导致方言识别准确率不高、鲁棒性差等问题。针对上述问题,提出一种结合残差网络(RestNet)和双向门控循环网络(BiGRU)的模型,该模型以GFCC特征图为输入,同时在残差网络中设计多尺度卷积模块,通过不同大小的卷积核提取特征,然后使用双向门控循环网络捕捉序列数据中的长期依赖关系,最后采用连接时序分类算法进行标签软对齐,实现四川方言语音识别模型。在四川方言语料库上的实验结果表明,提出的模型识别性能优于现有基准模型。

基于1DCNN-BiLSTM的航空发动机故障分类研究

随着航空发动机运行状态的变化,其故障模式也会发生变化。针对航空发动机的运行退化趋势,提出一种基于1DCNN-BiLSTM的航空发动机故障分类模型。该模型可以直接用于原始监测数据,不需要其他算法提取故障退化特征,并且能充分利用1DCNN提取时间维度局部特征的优势,以及BiLSTM处理非线性时间序列及利用双向上下文信息的特点,最后连接全连接层来学习双向时序依赖的特征信息,并使用softmax函数来诊断故障类别。在美国航空航天局公开的CMAPSS数据集上进行验证,将故障模式分为无故障、HPC故障(单一故障)、HPC&Fan故障(混合故障)三种类型。实验结果表明,与其他模型对比,所提模型具有较高的分类精度,这对提高航空发动机运行可靠性和进一步进行剩余使用寿命预测有一定的实用价值。

Early-season crop type mapping using 30-m reference time series

Early-season crop type mapping could provide important information for crop growth monitoring and yield prediction,but the lack of ground-surveyed training samples is the main challenge for crop type identification.Although reference time series based method(RBM)has been proposed to identify crop types without the use of ground-surveyed training samples,the methods are not suitable for study regions with small field size because the reference time series are mainly generated using data set with low spatial resolution.As the combination of Landsat data and Sentinel-2 data could increase the temporal resolution of 30-m image time series,we improved the RBM by generating reference normalized difference vegetation index(NDVI)/enhanced vegetation index(EVI)time series at 30-m resolution(30-m RBM)using both Landsat and Sentinel-2 data,then tried to estimate the potential of the reference NDVI/EVI time series for crop identification at early season.As a test case,we tried to use the 30-m RBM to identify major crop types in Hengshui,China at early season of 2018,the results showed that when the time series of the entire growing season were used for classification,overall classification accuracies of the 30-m RBM were higher than 95%,which were similar to the accuracies acquired using the ground-surveyed training samples.In addition,cotton,spring maize and summer maize distribution could be accurately generated 8,6 and 8 weeks before their harvest using the 30-m RBM;but winter wheat can only be accurately identified around the harvest time phase.Finally,NDVI outperformed EVI for crop type classification as NDVI had better separability for distinguishing crops at the green-up time phases.Comparing with the previous RBM,advantage of 30-m RBM is that the method could use the samples of the small fields to generate reference time series and process image time series with missing value for early-season crop casification;while,samples collected from multiple years should be futher used so that the reference time series could contain more crop growth conditions.

基于全局-局部散度的多元时间序列无监督降维方法

针对传统降维方法不能直接应用于多元时间序列,现有的多元时间序列降维方法难以在保证降维有效性的同时大幅降低数据维度的问题,提出一种基于全局-局部散度的多元时间序列无监督降维方法。首先,提出一种特征序列提取方法,提取多元时间序列协方差矩阵的上三角元素,将其组合为特征序列。然后,以“局部散度最小、全局散度最大”为基本思想,提出一种无监督降维模型,在保持局部近邻关系的同时,尽可能保留全局信息。将特征序列作为输入,最小化所有样本点邻域方差之和,最大化邻域中心点方差。求解模型得到的投影矩阵能够实现多元时间序列的降维。最后,在20组公开数据集上,对所提方法进行了实验验证。结果表明,所提方法能够在保证降维有效性的同时,较大幅度地降低多元时间序列的维度。

New supervised learning classifiers for structural damage diagnosis using time series features from a new feature extraction technique

The motivation for this article is to propose new damage classifiers based on a supervised learning problem for locating and quantifying damage.A new feature extraction approach using time series analysis is introduced to extract damage-sensitive features from auto-regressive models.This approach sets out to improve current feature extraction techniques in the context of time series modeling.The coefficients and residuals of the AR model obtained from the proposed approach are selected as the main features and are applied to the proposed supervised learning classifiers that are categorized as coefficient-based and residual-based classifiers.These classifiers compute the relative errors in the extracted features between the undamaged and damaged states.Eventually,the abilities of the proposed methods to localize and quantify single and multiple damage scenarios are verified by applying experimental data for a laboratory frame and a four-story steel structure.Comparative analyses are performed to validate the superiority of the proposed methods over some existing techniques.Results show that the proposed classifiers,with the aid of extracted features from the proposed feature extraction approach,are able to locate and quantify damage;however,the residual-based classifiers yield better results than the coefficient-based classifiers.Moreover,these methods are superior to some classical techniques.

基于TCN-LSTM模型的电网电能质量扰动分类研究

随着新能源发电和众多电动汽车充电桩等非线性设备并网运行,电网电能质量问题日渐凸显。现有解决方案在电能质量扰动分类上流程复杂,且在处理扰动信号时分类准确率偏低。为应对这一挑战,引入了TCN-LSTM混合模型,融合了时域卷积网络(temporal convolutional network,TCN)和长短时记忆网络(longshort-term memory,LSTM)。其中,TCN专注于捕捉时序数据的局部特性,而LSTM负责挖掘长期依赖关系,两者结合能够有效捕捉信号的局部特征和全局关系。为验证模型性能,对14种加入不同信噪比白噪声的电能质量扰动信号进行分类测试。结果表明,TCN-LSTM模型展现出较强的抗噪性能,并在与现有深度网络模型的对比中展现了更高的分类准确度。

SYNTHESIS AND CHARACTERIZATION OF A SERIES OF NOVEL INORGANIC MICROPOROUS CRYSTALS,BePO_4-CIn(n=4-7).

A series of novet beryllophosphate zeolites,named BePO_4-CIn(n=4-7), are synthesized hydrothermally and characterized with X-ray powder diffraction,IR spectra,SEM,thermat analysis and ion-exchange.

基于Sentinel-2破碎化地块灌区作物种植结构的提取

【目的】探究基于Sentinel-2遥感影像的决策树分类模型提取破碎化地块灌区作物种植结构的适用性。【方法】选取新疆阿拉沟灌区为研究区,以2021年覆盖作物全生育期的Sentinel-2遥感影像为数据源,结合田间调查和Google高清影像目视解译采样,基于主要作物物候信息、NDVI时序特征等分析确定作物识别的关键期阈值,构建决策树模型进行灌区主要作物分类,并对分类结果精度验证。【结果】基于Sentinel-2提取的灌区种植结构分布图地块纹理清晰,能够满足灌区用水管理需要;构建的决策树分类模型可在灌区尺度实现作物分类,方法简便易行,总体精度达到81.56%,Kappa系数为0.716 6。【结论】采用Sentinel-2遥感影像和决策树分类方法识别破碎化地块灌区复杂作物分类是可行的,可为灌区输配水决策和农业用水精细化管理提供基础信息。

The UCR Time Series Archive

The UCR time series archive–introduced in 2002,has become an important resource in the time series data mining community,with at least one thousand published papers making use of at least one data set from the archive.The original incarnation of the archive had sixteen data sets but since that time,it has gone through periodic expansions.The last expansion took place in the summer of 2015 when the archive grew from 45 to 85 data sets.This paper introduces and will focus on the new data expansion from 85 to 128 data sets.Beyond expanding this valuable resource,this paper offers pragmatic advice to anyone who may wish to evaluate a new algorithm on the archive.Finally,this paper makes a novel and yet actionable claim:of the hundreds of papers that show an improvement over the standard baseline(1-nearest neighbor classification),a fraction might be mis-attributing the reasons for their improvement.Moreover,the improvements claimed by these papers might have been achievable with a much simpler modification,requiring just a few lines of code.

基于Sentinel-2A时序谐波分析的山区林草资源遥感自动分类

文章提出了基于Sentinel-2A密集时序的山区林草资源自动分类方法。在GEE云计算平台支持下,首先基于Sentinel-2A影像计算年度NDVI密集时间序列;然后利用HANTS谐波分析对年度NDVI进行时序重构,获得年度完整的NDVI时序特征谱;在此基础上构建随机森林分类模型,通过特征计算和优选,完成影像分类和精度评价;并以大别山西麓麻城市为研究区开展了实验研究。实验结果表明:时序谐波分析方法能够有效地区分林草资源及森林亚类,时序谐波特征支持下Sentinel-2A密集时序林草资源遥感分类总体精度较高,相比传统多期分类、现有的全球30 m GLC_FCS30-2020分类产品,OA和Kappa均有了一定的提高。

An Improved Time Series Symbolic Representation Based on Multiple Features and Vector Frequency Difference

Symbolic Aggregate approXimation (SAX) is an efficient symbolic representation method that has been widely used in time series data mining. Its major limitation is that it relies exclusively on the mean values of segmented time series to derive the symbols. So, many important features of time series are not considered, such as extreme value, trend, fluctuation and so on. To solve this issue, we propose in this paper an improved Symbolic Aggregate approXimation based on multiple features and Vector Frequency Difference (SAX_VFD). SAX_VFD discriminates between time series by adopting an adaptive feature selection method. Furthermore, SAX_VFD is endowed with a new distance that takes into account the vector frequency difference between the symbolic sequence. We demonstrate the utility of the SAX_VFD on the time series classification task. The experimental results show that the proposed method has a better performance in terms of accuracy and dimensionality reduction compared to the so far published SAX based reduction techniques.

Study on Body Form and Garment Size Series of the Middle Age and Aged People

This paper was designed to analyze on the data, which was obtained from 'National Physique Fitness Investigation Report (2000)'. In order to get the typical body form and figure type of the middle age and aged people, it was focused on the body form data of this group (age 40 - 60). After calculation and analyzing, the distinguishing feature of body form and the distribution of figure type were deduced. Finally, the re-classification of body form for Chinese middle age and aged people was suggested. It as also suggested that a new garment size series especially for the middle age and aged should be built to fit for these people. This conclusion would be useful and significant to design and production for clothing company, especially that who take the aged people as their target consumer.